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Monday, 20 April 2020

Scatter Plots

There's been a huge increase in football related scatter plots recently. So as the guy who produced the first such plots, I thought I'd quickly run through why I thought this simple plot was useful and then try to expand the idea to provide additional usefulness.

The initial plots were designed to both inform and characterise playing style.

I think still the most successful plots use related metrics, for example expected assists and expected goals per 90 for individual players.

These "makers and takers" plots easily split players into those whose predominant talent is to create chances, those who get onto the end of opportunities and those rare players who excel at both disciplines.

Here's one for Arsenal 2019/20.

It's got sample size issues, but it's fairly evident that the creative players are towards the top left and the goal poachers are to be found in the bottom right.

Another quite neat aspect of this type of plot is that you can run a line through a player to the origin and any one with a similar ratio of xG and xA will lie close to that line.

In league wide samples, therefore you can find emerging players with similar qualities to the established stars.

There's a lot of data swilling around today, these plots are simple to make, three minutes tops, and with some thought about what you're trying to illustrate, they inform pretty well.

Over the weekend I came back to the idea, to see if I could add information that tells you a little bit more than just the raw connection between two metrics.

Here's what I came up with. It's again just a simple scatter plot, but I've used bubble size to introduce a third variable (metric volume per 90).

In addition I've used a single performance metric (NS xG added from ball carries) along the x axis and instead of plotting a complementary metric on the vertical axis, I've used a number to denote how diverse the x axis metrics are for each player.

This just plots the top 20 NS xG added by players through their ability to successfully carry the ball forward and move their team into a more dangerous pitch position.

It's a good one to chose because you know that Adama Traore will top the list (and he does).

Rather than a sterile scatter, you've now got a chart that not only tells you about a performance metric, it also instantly adds another layer (success volume) from which you can draw addition information about the characteristics of a player.

In short, those towards the right of the plot add more NS xG per 90 than others.
Larger bubble size indicates more successful progressive carries per 90.
And higher up the chart indicates more disorder and unpredictability in what a player will positively achieve for his team when on the all.

I've annotated players with the additional information you can draw from these plots.